Synergy of Remote Sensing and Geospatial Technologies to Advance Sustainable Development Goals for Future Coastal Urbanization and Environmental Challenges in a Riverine Megacity
Bibliographic record
Abstract
Riverine coastal megacities, particularly in semi-arid South Asian regions, face escalating environmental challenges due to rapid urbanization and climate change. While previous studies have examined urban growth patterns or environmental impacts independently, there remains a critical gap in understanding the integrated impacts of land use/land cover (LULC) changes on both ecosystem vulnerability and sustainable development achievements. This study addresses this gap through an innovative integration of multitemporal Landsat imagery (5, 7, and 8), SRTM-DEM, historical land use maps, and population data using the MOLUSCE plugin with cellular automata–artificial neural networks (CA-ANN) modelling to monitor LULC changes over three decades (1990–2020) and project future changes for 2025, 2030, and 2035, supporting the Sustainable Development Goals (SDGs) in Karachi, southern Pakistan, one of the world’s most populous megacities. The framework integrates LULC analysis with SDG metrics, achieving an overall accuracy greater than 97%, with user and producer accuracies above 77% and a Kappa coefficient approaching 1, demonstrating a high level of agreement. Results revealed significant urban expansion from 13.4% to 23.7% of the total area between 1990 and 2020, with concurrent reductions in vegetation cover, water bodies, and wetlands. Erosion along the riverbank has caused the Malir River’s area to decrease from 17.19 to 5.07 km2 by 2020, highlighting a key factor contributing to urban flooding during the monsoon season. Flood risk projections indicate that urbanized areas will be most affected, with 66.65% potentially inundated by 2035. This study’s innovative contribution lies in quantifying SDG achievements, showing varied progress: 26% for SDG 9 (Industry, Innovation, and Infrastructure), 18% for SDG 11 (Sustainable Cities and Communities), 13% for SDG 13 (Climate Action), and 16% for SDG 8 (Decent Work and Economic Growth). However, declining vegetation cover and water bodies pose challenges for SDG 15 (Life on Land) and SDG 6 (Clean Water and Sanitation), with 16% and 11%, respectively. This integrated approach provides valuable insights for urban planners, offering a novel framework for adaptive urban planning strategies and advancing sustainable practices in similar stressed megacity regions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".